Health Assessment and Life Prediction of cutting tools based on support vector regression.

International audienceThe integrity of machining tools is important to maintain a high level of surface quality. The wear of the tool can lead to poor surface quality of the workpiece and even to damage of the machine. Furthermore, in some applications such as aeronautics and precision engineering, it is preferable to change the tool earlier rather than to loose the workpiece because of its high price compared to the tool's one. Thus, to maintain a high quality of the manufactured pieces, it is necessary to assess and predict the level of wear of the cutting tool. This can be done by using condition monitoring and prognostics. The aim is then to estimate and predict the amount of wear and calculate the remaining useful life of the cutting tool. This paper presents a method for tool condition assessment and life prediction. The method is based on nonlinear feature reduction and support vector regression. The number of original features extracted from the monitoring signals is first reduced. These features are then used to learn nonlinear regression models to estimate and predict the level of wear. The method is applied on experimental data taken from a set of cuttings and simulation results are given. These results show that the proposed method is suitable for assessing the wear evolution of the cutting tools and predicting their remaining useful life. This information can then be used by the operators to take appropriate maintenance actions

Fault prognostic of bearings by using support vector data description.

International audienceThis paper presents a method for fault prognostic of bearings based on Principal Component Analysis (PCA) and Support Vector Data Description (SVDD). The purpose of the paper is to transform the monitoring vibration signals into features that can be used to track the health condition of bearings and to estimate their remaining useful life. PCA is used to reduce the dimensionality of original vibration features by removing the redundant ones. SVDD is a pattern recognition method based on structural risk minimization principles. In this contribution, the SVDD is used to fit the trained data to a hypersphere such that its radius can be used as a health indicator. The proposed method is then applied on real bearing degradation performed on an accelerated life test. The experimental results show that the health indicator reflects the bearing's degradation

Motor Bearing Failure Identification Using Multiple Long Short-Term Memory Training Strategies

In the context of condition-based maintenance of rotating machines in manufacturing systems, the early diagnosis of possible faults related to rolling elements of the bearing is mainly based on techniques from artificial intelligence, namely, Machine Learning (ML) and Deep Learning (DL). Approaches based on using Deep Learning methods have been the most coveted in recent years. Among a variety of models, the type of architecture known as Long-Short-Term Memory (LSTM) of Recurrent Neural Network (RNN) has both the ability to capture long-term dependencies and to adapt to sequential data modeling. It is therefore able to work on data without any preprocessing. This paper studies using four types of LSTM networks to diagnose bearing faults in a classification approach. It aims to intervene on both the input parameters and the network architecture, to achieve high performance. The proposed method is carried out in two different ways. In the first case, the data inputs are raw frames of vibration signals. However, in the second case, the network inputs are pre-computed time-frequency features. The results clearly showed that LSTMs are more accurate with the latter

Automated neural network optimization for data-driven predictive models:an application to ROP in drilling

The rate of penetration (ROP) holds significant importance in optimizing a drilling process. ROP assists in alleviating concerns in critical scenarios where limited visibility over explorations reduces efficiency, increases non-productive time, and heavily costs operations. In this study, a comprehensive automated data-driven model for ROP is proposed. The model integrates two optimization algorithms: the conjugate gradient algorithm (CG) for training and updating the parameters of the radial basis function neural network (RBF-NN) and the genetic algorithm (GA) for automated hyperparameter optimization (HPO). The proposed model is applied to a development well with six controllable drilling parameters as inputs and the results of comparison show prediction accuracy is improved by at least 50% compared to the RBF-NN. The findings of this study reveal the importance of parameter optimization and automated hyperparameter optimization to provide a more efficient, unbiased, and scalable approach, leading to improved generalization and performance of machine learning (ML) models. The developed model provides a basis for intelligent optimization and control in complex geological drilling processes

Analyse du comportement vibratoire des plaques fissurées par la X-FEM

Dans cet article, la méthode des éléments finis étendue (X-FEM) est utilisée pour décrire le comportement vibratoire des plaques présentant une discontinuité à différentes positions. La théorie utilisée est celle de Mindlin, où les effets de l'inertie de rotation et des déformations en cisaillement transverse sont pris en considération. Deux types d'éléments sont testés dans la discrétisation par éléments finis à savoir, élément quadratique à 4 nœuds et élément quadratique à 9 nœuds. La technique d'itération en sous espace est ensuite utilisée pour la résolution du problème aux valeurs propres. Différentes applications sont considérées, à savoir : cas des plaques avec fissure centrale et plaques avec fissure au bord. Les résultats obtenus en termes de fréquences propres et en fonction de la longueur de la fissure sont satisfaisants comparativement à ceux disponibles dans la littératur

Dynamic and fatigue modeling of cracked structures containing voids by XFEM

In this paper, we present quasi-static and dynamic modeling of linear elastic 2D structures containing simultaneously two types of material discontinuities, a void and a crack. The purpose of this modeling consists to evaluate the stress intensity factor (SIF) in dynamic and to predict the crack propagation in fatigue using the extended finite element method (X-FEM) [1] due to its high ability to treat material discontinuities without changing the regular meshing of the structure. This method is coupled with the interaction integral method [2] in the aim to quantify the SIF through the concept of the J integral. Some examples of validation of the computer code developed in this work were tested. The good correlation of the obtained results with the literature in dynamic (Song et al. [3]) and in fatigue (Giner et al. [4]) proves the effectiveness of the method as well as the developed computer code. As applications in the dynamic case, a parametric study on the presence, position and size of the void with respect to the crack and also on the crack type (crack edge and central crack) was conducted for some practical problems. References: [1] Belytschko T, Black T. ;Elastic crack growth in finite elements with minimal remishing. (1999), Int J numer Meth Engng, pp. 601-620. [2] Soheil Mohammadi: Extended Finite Element Method for Fracture Analysis of Structures, edité par Blackwell Publishing Ltd Singapore, (2008). [3] S.H. Song, G.H. Paulino, Dynamic stress intensity factors for homogeneous and smoothly heterogeneous materials using the interaction integral method, Int. J. Solids Struct. 43 (2006) 4830–4866 [4] E. Giner, N. Sukumar , J. E. Tarancon, F. J. Fuenmayor, An Abaqus implementation of the extended finite element method, Departamento de Ingenierıa Mecanica y de Materiales

Modélisation des fissures elliptiques et semi - elliptiques par hybridation des fonctions de poids dans le cas de la fatigue

Une méthode basée sur l'hybridation de fonctions de poids est utilisée pour décrire la propagation de fissures elliptiques ou semi-elliptiques par fatigue. Une comparaison avec une méthode basée sur les éléments finis montre la pertinence d'une telle approche

Analyse par la transformee d’ondelettes de delaminage et porosite dans les composites stratifies

Le recours aux ultrasons pour l’évaluation non destructive des composites stratifiés constitue un outil d’investigation privilégié pour la recherche, la détection et la caractérisation des défauts critiques dissimulés éventuellement dans ces matériaux. Les défauts recherchés sont les délaminages, les inclusions, les fissures et les porosités. Dans le cas de l’examen des plaques composites minces, la localisation d’un défaut interne ou proche de la surface fournit des signaux ultrasonores complexes dont l’analyse requiert des méthodes de traitement très avancées et plus raffinées que les techniques classiques. Cette étude porte sur l’analyse par ondelettes en intégrant le choix de l’ondelette analysante et le niveau de résolution. Elle est appliquée aux signaux ultrasonores obtenus lors du sondage de plaques minces stratifiées pour la détection et l’identification des défauts dans ces matériaux composites. Ils renferment des défauts incorporés ou artificiels (délaminages) et réels (porosités). Les coefficients calculés de la transformée en ondelettes ont été utilisés pour la détection et la localisation des positions de défauts dont les résultats sont en bon accord avec les données expérimentale